OpenRaiser/Intern-Atlas huggingface dataset:
Action and perception for spatiotemporal patterns
Venue: arXiv cs.AI 2017
Year: 2017

This is a contribution to the formalization of the concept of agents in multivariate Markov chains. Agents are commonly defined as entities that act, perceive, and are goal-directed. In a multivariate Markov chain (e.g. a cellular automaton) the transition matrix completely determines the dynamics. This seems to contradict the possibility of acting entities within such a system. Here we present definitions of actions and perceptions within multivariate Markov chains based on entity-sets. Entity-sets represent a largely independent choice of a set of spatiotemporal patterns that are considered as all the entities within the Markov chain. For example, the entity-set can be chosen according to operational closure conditions or complete specific integration. Importantly, the perception-action loop also induces an entity-set and is a multivariate Markov chain. We then show that our definition of actions leads to non-heteronomy and that of perceptions specialize to the usual concept of perception in the perception-action loop.
Streaming Non-monotone Submodular Maximization: Personalized Video Summarization on the Fly
Venue: arXiv cs.DS 2017
Year: 2017

The need for real time analysis of rapidly producing data streams (e.g., video and image streams) motivated the design of streaming algorithms that can efficiently extract and summarize useful information from massive data "on the fly". Such problems can often be reduced to maximizing a submodular set function subject to various constraints. While efficient streaming methods have been recently developed for monotone submodular maximization, in a wide range of applications, such as video summarization, the underlying utility function is non-monotone, and there are often various constraints imposed on the optimization problem to consider privacy or personalization. We develop the first efficient single pass streaming algorithm, Streaming Local Search, that for any streaming monotone submodular maximization algorithm with approximation guarantee $α$ under a collection of independence systems ${\cal I}$, provides a constant $1/\big(1+2/\sqrtα+1/α+2d(1+\sqrtα)\big)$ approximation guarantee for maximizing a non-monotone submodular function under the intersection of ${\cal I}$ and $d$ knapsack constraints. Our experiments show that for video summarization, our method runs more than 1700 times faster than previous work, while maintaining practically the same performance.
Image Crowd Counting Using Convolutional Neural Network and Markov Random Field
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose a method called Convolutional Neural Network-Markov Random Field (CNN-MRF) to estimate the crowd count in a still image. We first divide the dense crowd visible image into overlapping patches and then use a deep convolutional neural network to extract features from each patch image, followed by a fully connected neural network to regress the local patch crowd count. Since the local patches have overlapping portions, the crowd count of the adjacent patches has a high correlation. We use this correlation and the Markov random field to smooth the counting results of the local patches. Experiments show that our approach significantly outperforms the state-of-the-art methods on UCF and Shanghaitech crowd counting datasets.
SmoothGrad: removing noise by adding noise
Venue: arXiv cs.LG 2017
Year: 2017

Explaining the output of a deep network remains a challenge. In the case of an image classifier, one type of explanation is to identify pixels that strongly influence the final decision. A starting point for this strategy is the gradient of the class score function with respect to the input image. This gradient can be interpreted as a sensitivity map, and there are several techniques that elaborate on this basic idea. This paper makes two contributions: it introduces SmoothGrad, a simple method that can help visually sharpen gradient-based sensitivity maps, and it discusses lessons in the visualization of these maps. We publish the code for our experiments and a website with our results.
Fast Maximum-Likelihood Decoder for 4*4 Quasi-Orthogonal Space-Time Block Code
Venue: arXiv cs.IT 2017
Year: 2017

This letter introduces two fast maximum-likelihood (ML) detection methods for 4*4 quasi-orthogonal space-time block code (QOSTBC). The first algorithm with a relatively simple design exploits structure of quadrature amplitude modulation (QAM) constellations to achieve its goal and the second algorithm, though somewhat more complex, can be applied to any arbitrary constellation. Both decoders utilize a novel decomposition technique for ML metric which divides the metric into independent positive parts and a positive interference part. Search spaces of symbols are substantially reduced by employing the independent parts and statistics of noise. Finally, the members of search spaces are successively evaluated until the metric is minimized. Simulation results confirm that the proposed decoder is superior to some of the most recently published methods in terms of complexity level. More specifically, the results verified that application of the new algorithm with 1024-QAM would require reduced computational complexity compared to state-of-the-art solution with 16-QAM.
"(Weitergeleitet von Journalistin)": The Gendered Presentation of Professions on Wikipedia
Venue: arXiv cs.CY 2017
Year: 2017

Previous research has shown the existence of gender biases in the depiction of professions and occupations in search engine results. Such an unbalanced presentation might just as likely occur on Wikipedia, one of the most popular knowledge resources on the Web, since the encyclopedia has already been found to exhibit such tendencies in past studies. Under this premise, our work assesses gender bias with respect to the content of German Wikipedia articles about professions and occupations along three dimensions: used male vs. female titles (and redirects), included images of persons, and names of professionals mentioned in the articles. We further use German labor market data to assess the potential misrepresentation of a gender for each specific profession. Our findings in fact provide evidence for systematic over-representation of men on all three dimensions. For instance, for professional fields dominated by females, the respective articles on average still feature almost two times more images of men; and in the mean, 83% of the mentioned names of professionals were male and only 17% female.
Subspace Clustering via Optimal Direction Search
Venue: arXiv cs.CV 2017
Year: 2017

This letter presents a new spectral-clustering-based approach to the subspace clustering problem. Underpinning the proposed method is a convex program for optimal direction search, which for each data point d finds an optimal direction in the span of the data that has minimum projection on the other data points and non-vanishing projection on d. The obtained directions are subsequently leveraged to identify a neighborhood set for each data point. An alternating direction method of multipliers framework is provided to efficiently solve for the optimal directions. The proposed method is shown to notably outperform the existing subspace clustering methods, particularly for unwieldy scenarios involving high levels of noise and close subspaces, and yields the state-of-the-art results for the problem of face clustering using subspace segmentation.
Modelling prosodic structure using Artificial Neural Networks
Venue: arXiv cs.CL 2017
Year: 2017

The ability to accurately perceive whether a speaker is asking a question or is making a statement is crucial for any successful interaction. However, learning and classifying tonal patterns has been a challenging task for automatic speech recognition and for models of tonal representation, as tonal contours are characterized by significant variation. This paper provides a classification model of Cypriot Greek questions and statements. We evaluate two state-of-the-art network architectures: a Long Short-Term Memory (LSTM) network and a convolutional network (ConvNet). The ConvNet outperforms the LSTM in the classification task and exhibited an excellent performance with 95% classification accuracy.
Distributed Subgraph Detection
Venue: arXiv cs.DC 2017
Year: 2017

In the standard CONGEST model for distributed network computing, it is known that "global" tasks such as minimum spanning tree, diameter, and all-pairs shortest paths, consume large bandwidth, for their running-time is $Ω(\mbox{poly}(n))$ rounds in $n$-node networks with constant diameter. Surprisingly, "local" tasks such as detecting the presence of a 4-cycle as a subgraph also requires $\widetildeΩ(\sqrt{n})$ rounds, even using randomized algorithms, and the best known upper bound for detecting the presence of a 3-cycle is $\widetilde{O}(n^{\frac{2}{3}})$ rounds. The objective of this paper is to better understand the landscape of such subgraph detection tasks. We show that, in contrast to \emph{cycles}, which are hard to detect in the CONGEST model, there exists a deterministic algorithm for detecting the presence of a subgraph isomorphic to $T$ running in a \emph{constant} number of rounds, for every tree $T$. Our algorithm provides a distributed implementation of a combinatorial technique due to Erdős et al. for sparsening the set of partial solutions kept by the nodes at each round. Our result has important consequences to \emph{distributed property-testing}, i.e., to randomized algorithms whose aim is to distinguish between graphs satisfying a property, and graphs far from satisfying that property. In particular, we get that, for every graph pattern $H$ composed of an edge and a tree connected in an arbitrary manner, there exists a distributed testing algorithm for $H$-freeness, performing in a constant number of rounds. Although the class of graph patterns $H$ formed by a tree and an edge connected arbitrarily may look artificial, all previous results of the literature concerning testing $H$-freeness for classical patterns such as cycles and cliques can be viewed as direct consequences of our result, while our algorithm enables testing more complex patterns.
Recurrent Latent Variable Networks for Session-Based Recommendation
Venue: arXiv cs.IR 2017
Year: 2017

In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques.
Interactive Shape Perturbation
Venue: arXiv cs.GR 2017
Year: 2017

We present a web application for the procedural generation of perturbations of 3D models. We generate the perturbations by generating vertex shaders that change the positions of vertices that make up the 3D model. The vertex shaders are created with an interactive genetic algorithm, which displays to the user the visual effect caused by each vertex shader, allows the user to select the visual effect the user likes best, and produces a new generation of vertex shaders using the user feedback as the fitness measure of the genetic algorithm. We use genetic programming to represent each vertex shader as a computer program. This paper presents details of requirements specification, software architecture, high and low-level design, and prototype user interface. We discuss the project's current status and development challenges.
Zero-Shot Relation Extraction via Reading Comprehension
Venue: arXiv cs.CL 2017
Year: 2017

We show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot. This reduction has several advantages: we can (1) learn relation-extraction models by extending recent neural reading-comprehension techniques, (2) build very large training sets for those models by combining relation-specific crowd-sourced questions with distant supervision, and even (3) do zero-shot learning by extracting new relation types that are only specified at test-time, for which we have no labeled training examples. Experiments on a Wikipedia slot-filling task demonstrate that the approach can generalize to new questions for known relation types with high accuracy, and that zero-shot generalization to unseen relation types is possible, at lower accuracy levels, setting the bar for future work on this task.
Online Learning for Structured Loss Spaces
Venue: arXiv cs.LG 2017
Year: 2017

We consider prediction with expert advice when the loss vectors are assumed to lie in a set described by the sum of atomic norm balls. We derive a regret bound for a general version of the online mirror descent (OMD) algorithm that uses a combination of regularizers, each adapted to the constituent atomic norms. The general result recovers standard OMD regret bounds, and yields regret bounds for new structured settings where the loss vectors are (i) noisy versions of points from a low-rank subspace, (ii) sparse vectors corrupted with noise, and (iii) sparse perturbations of low-rank vectors. For the problem of online learning with structured losses, we also show lower bounds on regret in terms of rank and sparsity of the source set of the loss vectors, which implies lower bounds for the above additive loss settings as well.
Prediction of Muscle Activations for Reaching Movements using Deep Neural Networks
Venue: arXiv cs.NE 2017
Year: 2017

The motor control problem involves determining the time-varying muscle activation trajectories required to accomplish a given movement. Muscle redundancy makes motor control a challenging task: there are many possible activation trajectories that accomplish the same movement. Despite this redundancy, most movements are accomplished in highly stereotypical ways. For example, point-to-point reaching movements are almost universally performed with very similar smooth trajectories. Optimization methods are commonly used to predict muscle forces for measured movements. However, these approaches require computationally expensive simulations and are sensitive to the chosen optimality criteria and regularization. In this work, we investigate deep autoencoders for the prediction of muscle activation trajectories for point-to-point reaching movements. We evaluate our DNN predictions with simulated reaches and two methods to generate the muscle activations: inverse dynamics (ID) and optimal control (OC) criteria. We also investigate optimal network parameters and training criteria to improve the accuracy of the predictions.
Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach
Venue: arXiv cs.CR 2017
Year: 2017

The evolution of mobile malware poses a serious threat to smartphone security. Today, sophisticated attackers can adapt by maximally sabotaging machine-learning classifiers via polluting training data, rendering most recent machine learning-based malware detection tools (such as Drebin, DroidAPIMiner, and MaMaDroid) ineffective. In this paper, we explore the feasibility of constructing crafted malware samples; examine how machine-learning classifiers can be misled under three different threat models; then conclude that injecting carefully crafted data into training data can significantly reduce detection accuracy. To tackle the problem, we propose KuafuDet, a two-phase learning enhancing approach that learns mobile malware by adversarial detection. KuafuDet includes an offline training phase that selects and extracts features from the training set, and an online detection phase that utilizes the classifier trained by the first phase. To further address the adversarial environment, these two phases are intertwined through a self-adaptive learning scheme, wherein an automated camouflage detector is introduced to filter the suspicious false negatives and feed them back into the training phase. We finally show that KuafuDet can significantly reduce false negatives and boost the detection accuracy by at least 15%. Experiments on more than 250,000 mobile applications demonstrate that KuafuDet is scalable and can be highly effective as a standalone system.
Automatic Localization of Deep Stimulation Electrodes Using Trajectory-based Segmentation Approach
Venue: arXiv cs.CV 2017
Year: 2017

Parkinson's disease (PD) is a degenerative condition of the nervous system, which manifests itself primarily as muscle stiffness, hypokinesia, bradykinesia, and tremor. In patients suffering from advanced stages of PD, Deep Brain Stimulation neurosurgery (DBS) is the best alternative to medical treatment, especially when they become tolerant to the drugs. This surgery produces a neuronal activity, a result from electrical stimulation, whose quantification is known as Volume of Tissue Activated (VTA). To locate correctly the VTA in the cerebral volume space, one should be aware exactly the location of the tip of the DBS electrodes, as well as their spatial projection. In this paper, we automatically locate DBS electrodes using a threshold-based medical imaging segmentation methodology, determining the optimal value of this threshold adaptively. The proposed methodology allows the localization of DBS electrodes in Computed Tomography (CT) images, with high noise tolerance, using automatic threshold detection methods.
Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Early detection of pulmonary cancer is the most promising way to enhance a patient's chance for survival. Accurate pulmonary nodule detection in computed tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In this paper, inspired by the successful use of deep convolutional neural networks (DCNNs) in natural image recognition, we propose a novel pulmonary nodule detection approach based on DCNNs. We first introduce a deconvolutional structure to Faster Region-based Convolutional Neural Network (Faster R-CNN) for candidate detection on axial slices. Then, a three-dimensional DCNN is presented for the subsequent false positive reduction. Experimental results of the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior detection performance of the proposed approach on nodule detection(average FROC-score of 0.891, ranking the 1st place over all submitted results).
Photo-realistic Facial Texture Transfer
Venue: arXiv cs.CV 2017
Year: 2017

Style transfer methods have achieved significant success in recent years with the use of convolutional neural networks. However, many of these methods concentrate on artistic style transfer with few constraints on the output image appearance. We address the challenging problem of transferring face texture from a style face image to a content face image in a photorealistic manner without changing the identity of the original content image. Our framework for face texture transfer (FaceTex) augments the prior work of MRF-CNN with a novel facial semantic regularization that incorporates a face prior regularization smoothly suppressing the changes around facial meso-structures (e.g eyes, nose and mouth) and a facial structure loss function which implicitly preserves the facial structure so that face texture can be transferred without changing the original identity. We demonstrate results on face images and compare our approach with recent state-of-the-art methods. Our results demonstrate superior texture transfer because of the ability to maintain the identity of the original face image.
Multi-Lane Perception Using Feature Fusion Based on GraphSLAM
Venue: arXiv cs.RO 2017
Year: 2017

An extensive, precise and robust recognition and modeling of the environment is a key factor for next generations of Advanced Driver Assistance Systems and development of autonomous vehicles. In this paper, a real-time approach for the perception of multiple lanes on highways is proposed. Lane markings detected by camera systems and observations of other traffic participants provide the input data for the algorithm. The information is accumulated and fused using GraphSLAM and the result constitutes the basis for a multilane clothoid model. To allow incorporation of additional information sources, input data is processed in a generic format. Evaluation of the method is performed by comparing real data, collected with an experimental vehicle on highways, to a ground truth map. The results show that ego and adjacent lanes are robustly detected with high quality up to a distance of 120 m. In comparison to serial lane detection, an increase in the detection range of the ego lane and a continuous perception of neighboring lanes is achieved. The method can potentially be utilized for the longitudinal and lateral control of self-driving vehicles.
Alignment Distances on Systems of Bags
Venue: arXiv cs.CV 2017
Year: 2017

Recent research in image and video recognition indicates that many visual processes can be thought of as being generated by a time-varying generative model. A nearby descriptive model for visual processes is thus a statistical distribution that varies over time. Specifically, modeling visual processes as streams of histograms generated by a kernelized linear dynamic system turns out to be efficient. We refer to such a model as a System of Bags. In this work, we investigate Systems of Bags with special emphasis on dynamic scenes and dynamic textures. Parameters of linear dynamic systems suffer from ambiguities. In order to cope with these ambiguities in the kernelized setting, we develop a kernelized version of the alignment distance. For its computation, we use a Jacobi-type method and prove its convergence to a set of critical points. We employ it as a dissimilarity measure on Systems of Bags. As such, it outperforms other known dissimilarity measures for kernelized linear dynamic systems, in particular the Martin Distance and the Maximum Singular Value Distance, in every tested classification setting. A considerable margin can be observed in settings, where classification is performed with respect to an abstract mean of video sets. For this scenario, the presented approach can outperform state-of-the-art techniques, such as Dynamic Fractal Spectrum or Orthogonal Tensor Dictionary Learning.
Hybrid Collaborative Recommendation via Semi-AutoEncoder
Venue: arXiv cs.IR 2017
Year: 2017

In this paper, we present a novel structure, Semi-AutoEncoder, based on AutoEncoder. We generalize it into a hybrid collaborative filtering model for rating prediction as well as personalized top-n recommendations. Experimental results on two real-world datasets demonstrate its state-of-the-art performances.